CN115358540A

CN115358540A - Full-period carbon evaluation method and system for urban update project

Info

Publication number: CN115358540A
Application number: CN202210907939.0A
Authority: CN
Inventors: 邓军; 鲍涵; 江玉; 陈洪波; 钱征寒; 李榕东; 于子鳌; 张莞莅; 张莹; 王丹; 胡妮妮
Original assignee: Shenzhen Planning And Design Consulting Ltd By Share Ltd
Current assignee: Shenzhen Planning And Design Consulting Ltd By Share Ltd
Priority date: 2022-07-29
Filing date: 2022-07-29
Publication date: 2022-11-18

Abstract

The invention discloses a full-period carbon evaluation method for an urban updating project, which comprises the following steps: preprocessing the acquired data based on an AHP analytic hierarchy process, performing basic analysis on the preprocessed acquired data, and classifying and distinguishing the data attribute characteristic values of the acquired data according to the characteristic value distinguishing standard of the basic characteristic data and the characteristic value distinguishing standard of the real-time characteristic data by using the distinguishing method of each attribute characteristic value of the basic characteristic data and the real-time characteristic data to form an attribute characteristic value set reflecting urban data; forming an evaluation index system by combining a corresponding function model in the collected data with the attribute characteristic value set AES; constructing an urban space diagnosis model, wherein the urban space diagnosis model comprises a mapping relation between acquired data and basic analysis; and (4) carrying out data analysis by using the urban space diagnosis model. The invention changes the existing planning working method depending on individual subjective experience into objectification, scientification and quantitative transformation through an original urban space diagnosis model.

Description

Full-period carbon evaluation method and system for urban update project

Technical Field

The invention relates to the technical field of carbon emission evaluation, in particular to a full-period carbon evaluation method and system for an urban updating project.

Background

The current urban physical examination has three typical practices, including 'examination type' urban physical examination proposed by two committees, 'monitoring type' physical examination realized by utilizing information technologies such as big data, twin cities and the like, and a 'evaluation type' physical examination evaluation system constructed by fusing methods such as various data application data statistics, image recognition and the like. Three city physical examination methods are developed and described below, and the advantages and disadvantages of the methods are evaluated in combination with cases.

(1) The "examination" physical examination is the urban physical examination assessment respectively dominated by the housing department and the natural resources department.

The urban physical examination of the urban construction department focuses on short slabs and defects in the aspects of urban quality and urban construction, the problems of urban diseases and the like existing in cities are solved, the urban management is emphasized, and the urban area and the urban construction area scale are mainly concerned. The urban physical examination of the natural resource department focuses on monitoring the execution conditions of the constraint indexes and the mandatory contents of the territorial space planning, and scientifically evaluating the planning implementation performance.

The urban physical examination index content of the assessment formula is mostly statistical data, including but not limited to national and state survey, annual change survey data, natural resource special survey, statistical yearbook and the like. Data typically originates from various authorities. The limiting factors of the physical examination are mainly that indexes of the physical examination do not concern the overall operation of urban space, and do not directly point to urban problems, and a technical route from characterization, etiology and solution is not formed.

(2) "panel" physical examination

The main contents of the 'scale type' urban physical examination are that a scale model is built by collecting and processing data of a large number of administrative regions to carry out scale rating, a 'scale type' physical examination evaluation system is built by using various methods such as various data, application space statistical analysis, image recognition, natural language processing and the like, and main data sources include but are not limited to government affair data, POI data, GPS track data, LBS positioning data, road and street view data, remote sensing image data, enterprise and industry and commerce registration data and open data such as internet renting, recruitment, assessment and the like.

The limiting factors of such physical examination methods are two aspects, on one hand, the difficulty in acquiring government affair data is relatively high for general towns; on the other hand, the index system of the evaluation type physical examination does not directly point to the urban problem, and a technical route of solution from characterization-cause-is lacked, so that a corresponding urban problem solution is difficult to put forward.

(3) Construction of monitoring-type urban physical examination-urban physical examination evaluation information platform

The main content of the monitoring type urban physical examination is to build an urban dynamic information platform (table 1-2), realize dynamic update of urban space data and master urban space development rules and dynamics. The physical examination mode tends to evaluate the current situation of urban development, track the annual progress of urban space data and master the evolution law of urban space. The data mainly comes from government affair data and various big data.

The urban physical examination mode is also limited in that the difficulty in acquiring government affair data is high, physical examination contents are only limited to planning and monitoring aiming at urban core indexes, and a technical route of a solution from characterization, etiology and disease is lacked, so that a corresponding urban problem solution is difficult to provide.

The existing urban physical examination mode is difficult to provide a path from urban problem representation to urban etiology to a space solution for solving the urban problem, and the reason is the target-oriented working logic. In addition, although the urban development indexes are of a complicated type, the starting point of the urban space diagnosis system is the problem surrounding urban spaces, especially urban construction spaces.

Reviewing and summarizing the evolution process of the urban physical examination work in China, and then finding out that the most key problem of the urban physical examination is the guidance problem by means of theory and practice in the field of phase discrimination, namely: is the urban physical examination more to enhance blueprint planning monitoring and implement upper assessment index service, or to discover problems in urban operation, promote urban progressive improvement and promotion service? Around this key problem (which can also be said to be the judgment of the value of the urban physical examination), the current urban physical examination mainly has the following problems:

(1) Physical examination index static machine: an index system is generally expanded according to five major development concepts, and the implementation of a static planning target is emphasized and disconnected with the actual running state of a city. For example, a school's hardware complement may meet planning requirements, but the actual situation and quality of the teaching activities are not reflected.

(2) The dimension scale is relatively single: the evaluation method mainly focuses on the evaluation of comprehensive indexes of the whole city level, but the physical examination research of the parcel and community level is insufficient, and the evaluation of focusing on city space problems (namely, the problems which can be practically solved through planning and construction) is relatively less.

(3) "health criteria" are more fuzzy: since most physical examination indicators are result-oriented in terms of assessment of planning objectives, the health level of urban development is often characterized by the achievement rate of the target objectives (e.g., 70% of the predicted objective of 2035 for human-average GDP), but this "standard" is not necessarily scientific, reflecting the characteristics of urban development rather than the problem. Even though some of the indicators are based on the planning standard specifications, the appearance of new space and emerging requirements, as well as the influence of spatio-temporal factors, make the traditional planning standards questionable in real concrete situations.

(4) The new technology application is not developed enough: at present, urban physical examination evaluation based on new technologies such as big data and new data application has considerable development, but the urban physical examination evaluation method mainly expands the data sources of urban physical examination and strengthens the real-time monitoring capability. However, model innovation and index research of physical examination evaluation are still far from enough, and particularly, the model innovation and index research are influenced by factors such as data barriers among government departments, and the data are not really and fully combined with characteristics of urban space elements (such as land, buildings, facilities, crowds and activities).

(5) The system mechanism is to be perfected: according to the requirements of the planned management and planning, the working mechanism of combining the urban physical examination and the follow-up work is not clear enough. Sometimes, the physical examination is performed for the physical examination, and the urban physical examination becomes a task required to be completed by the superior, rather than a prepositive need for making other tasks.

In addition, the AHP (Analytic Hierarchy Process, AHP for short) Analytic Hierarchy Process was proposed by american operational scientists, professor t.l.saaty university of pittsburgh in the early 70 s of the 20 th century, and the AHP Analytic Hierarchy Process is a simple, flexible and practical multi-criterion decision-making method for quantitative analysis of qualitative problems. The method is characterized in that various factors in the complex problem are classified into interconnected ordered levels to be organized, expert opinions and objective judgment results of an analyst are directly and effectively combined according to a certain objective and realistic subjective judgment structure (mainly pairwise comparison), and the importance of pairwise comparison of elements of one level is quantitatively described. Then, a weight value reflecting the relative importance order of each layer element is calculated by a mathematical method, and the relative weight of all the elements is calculated through the total ordering among all the layers and the elements are ordered. Since the method is introduced to China in 1982, the method is rapidly and widely valued and applied in various fields of social and economic in China, such as energy system analysis, city planning, economic management, scientific research evaluation and the like, by the characteristics that qualitative analysis and quantitative analysis are combined to process various decision factors and the advantages that the system is flexible and simple.

The concept of urban space diagnosis is provided, the working logic is switched from target-oriented to action-oriented, the emphasis is on finding urban space problems by using what method, and then how to guide the space problems to the corresponding space strategy, and a carbon emission evaluation strategy is realized based on AHP and double control principle.

Disclosure of Invention

The following presents a simplified summary of embodiments of the invention in order to provide a basic understanding of some aspects of the invention. It should be understood that the following summary is not an exhaustive overview of the invention. It is not intended to determine the key or critical elements of the present invention, nor is it intended to limit the scope of the present invention. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later.

According to one aspect of the application, a method for full-cycle carbon assessment of a city update project is provided, which comprises the following steps:

step 1: preprocessing the collected data based on an AHP analytic hierarchy process, wherein the preprocessing specifically comprises the following steps: collecting city data to be diagnosed, wherein the city data comprises basic characteristic data and real-time characteristic data; the basic data is the existing data recorded in the urban records, and the real-time data is the data changed in real time or the data of a preset target (which can have certain subjectivity);

step 2: performing basic analysis on the preprocessed acquired data: the acquired data comprises basic data and real-time data, the basic data and the real-time data comprise various data with attributes divided according to space types, and the basic analysis comprises function models corresponding to the data with different attributes in the acquired data;

and step 3: using the basic feature data sumThe method for judging each Attribute characteristic value of real-time characteristic data is characterized in that the classification and the judgment of the data Attribute characteristic values are carried out on the collected data according to the characteristic value judgment standard of basic characteristic data and the characteristic value judgment standard of the real-time characteristic data, and an Attribute characteristic value set AES (Attribute eigenvalue set) = { m 'embodying city data is formed' ₁ ,m’ ₂ ,…m’ _x ,n’ ₁ ,n’ ₂ ,…n’ _y And storing the data in a database, wherein x and y are natural numbers; wherein m' ₁ ,m’ ₂ ,…m’ _x Is base characteristic data, n' ₁ ,n’ ₂ ,…n’ _y Real-time characteristic data;

and 4, step 4: forming an evaluation index system by using function models corresponding to data with different attributes in the acquired data and combining the attribute characteristic value set AES;

and 5: constructing an urban space diagnosis model by combining the acquired data in the step 1, the basic analysis in the step 2, the attribute feature value set AES in the step 3 and the evaluation index system in the step 4, wherein the urban space diagnosis model comprises a mapping relation between the acquired data and the basic analysis;

and 6: the method comprises the following steps of utilizing a city space diagnosis model to analyze data, selecting one dimension or multiple dimensions to analyze, and specifically: during data analysis, a characteristic value set of a certain dimension and a corresponding evaluation index system are obtained and stored, and city evaluation data GGI = { g = 'in the dimension is obtained' ₁ ,g’ ₂ ,…g’ _z }。

In addition, the above-mentioned process of this application can show through visual interface, and during the use, the technical staff only need input relevant attribute value and its corresponding index threshold value according to self needs, then can obtain this data analysis result. In addition, the visual interface can also be used for displaying real-time operation information of the simulation model or curve change of the mathematical model.

Further, the method for analyzing data by using the urban space diagnosis model specifically comprises the following steps:

step A: the city data comprises a basic characteristic data set M and a real-time characteristic data set N, wherein M is marked as { M ₁ ,M ₂ ,…M _i ,…,M _x In which M _i Is given as { m } _i1 ,m _i2 ,m _i3 ,…,m _iti The list is expressed as follows:

wherein m is _ij Is the basic data attribute M _i Attribute feature value of (1), m _ij (i＝1,…,x；j＝1,…,t _i ) In the subscript (a), i means the i-th basic data attribute, and j means M _i J-th attribute feature value, t, of an attribute _i Is represented by M _i The number of attribute feature values of (2); eigenvalue discrimination method Δ _i (i =1, \ 8230;, x) is the discrimination base data attribute group M _i (i =1, \8230;, x) a method of each characteristic value; the evaluation index set is a data set obtained by combining function models corresponding to data with different attributes in the collected data with the attribute characteristic value set; eigenvalue discrimination standard sigma _ij Is to correspond to an underlying data attribute M _i Confirming each characteristic value m _ij (i＝1,…,x；j＝1,…,t _i ) A judgment standard value of (2);

and B, step B: the real-time feature data set N is denoted as { N } ₁ ,N ₂ ,…N _i ,…,N _y In which N is _i Is marked as n _i1 ,n _i2 ,n _i3 ,…,n _iri The list is expressed as follows:

wherein n is _ij Is running data attribute N _i N of the attribute feature value of _ij (i＝1,…,y；j＝1,…,r _i ) In the subscript (i) means the i-th operation data attribute, and j means N _i J-th attribute feature value of attribute, r _i Is represented by N _i The number of attribute feature values of (2). Eigenvalue discrimination method Δ _x+i (i =1, \8230;, y) is the discriminating underlying data attribute group N _i (i =1, \8230;, y) a method of each characteristic value; the evaluation index set is a data set obtained by combining function models corresponding to data with different attributes in the collected data with the attribute characteristic value set; criterion η for discriminating characteristic value _ij Is to N _i Attribute validation each eigenvalue n _ij The discrimination standard value of (1);

step C: city evaluation data GGI, namely evaluation index system G, and is marked as G ₁ ,G ₂ ,…G _i ,…,G _z }; wherein G is _i Is marked as { g _i1 ,g _i2 ,g _i3 ,…,g _ipi },G _i The attribute feature value of (D) and the evaluation index set D of the basic feature data set M _i And an evaluation index set D of the real-time characteristic data set N _X+i Related, the list is as follows:

evaluation index System G	Attribute eigenvalues
		G ₁	g ₁₁ ,g ₁₂ ,g ₁₃ ,…,g _1p1
G ₂	g ₂₁ ,g ₂₂ ,g ₂₃ ,…,g _2p2
		G _i	g _i1 ,g _i2 ,g _i3 ,…,g _ipi
…	…
		G _z	g _z1 ,g _z2 ,g _z3 ,…,g _zpz

Wherein, g _ij Is G _i The attribute characteristic values listed by the city data characteristic evaluation attribute are specifically the evaluation index set D of the basic characteristic data set M _i And an evaluation index set D of the real-time characteristic data set N _X+i The corresponding sums are obtained, g _ij (i＝1,…,z；j＝1,…,p _i ) In the subscript (a), i means the characteristic evaluation attribute of the ith city data, and j means G _i J-th attribute feature value, p, of an attribute _i Is represented by G _i The number of attribute feature values of (2). i, j, p _i And z is a natural number.

And finally, constructing a comprehensive evaluation function according to the urban evaluation data GGI, and calculating a carbon emission evaluation result according to the comprehensive evaluation function and the urban actual operation level.

Further, the basic data is classified into data of 6 attributes by space type, and the 6 attributes are existing recorded data based on land, existing recorded data based on buildings, existing recorded data based on road network, existing recorded data based on population, existing recorded data based on enterprises, and existing recorded data based on facilities, respectively. The real-time feature data is also classified into data of 6 attributes by spatial type, and the 6 attributes are estimated data based on a land, a building, a road network, a population, a business and a facility. The expected data can be data planned to be realized, data of a preset target or data which is calculated to be changed in real time.

Correspondingly, the basic analysis is divided into land general analysis, building general analysis, road network general analysis, population general analysis, industry general analysis and facility general analysis corresponding to the six attribute basic data.

The method comprises the steps of refining urban data with 6 attributes according to the actual running condition of a city to form a multi-level urban data set, correspondingly forming multi-level analysis according to the multi-level urban data set after grade refinement, and specifically comprising green space and open space special analysis, public service facility special analysis, municipal facility special analysis, traffic facility special analysis, commercial facility special analysis, residence special analysis, storage factory special analysis and office building special analysis, and also comprising skyline analysis, city space structure analysis, life circle analysis, position balance analysis, city vitality analysis, TOD analysis, industry gathering area analysis, ecological pattern analysis and the like which are developed by combining a high-level urban concept.

Of course, the above-mentioned city data and corresponding basic evaluation are a specific implementation, and when evaluating other planning aspects of a city, it may also be divided into various data of other attributes and corresponding basic evaluation according to the space type.

As a specific implementation scheme, the mapping relationship between city data and basic analysis is described by taking building-based data as an example: the building-based data comprises seismic motion levels of the multi-exposure intensity, the basic intensity, the rare exposure intensity and the super-fortification intensity of the existing building and the prepared building location, the structural behavior parameters comprising the multi-exposure intensity, the basic intensity, the rare exposure intensity and the super-fortification intensity are basically analyzed, and the mapping relation is a seismic motion curve of the multi-exposure intensity, the basic intensity, the rare exposure intensity and the super-fortification intensity established according to the structural behavior parameters.

Furthermore, the urban space diagnosis model integrates a space quantitative analysis method which absorbs the frontier, and a dynamically-evaluated urban space diagnosis index system is built.

The urban space diagnosis model is divided into three sequential progressive flows according to the direction for solving the space problem: spatial analysis, problem troubleshooting and spatial evolution; the spatial analysis aims to solve the problem of how to quantify a pervasive area in urban space to analyze urban space information; the troubleshooting problem is how to solve and identify the urban space development problem; the space evolution is just to solve the problem of how to lead to the space planning strategy when identifying the problem.

The urban space diagnosis model comprises a space analysis module, a problem troubleshooting module and a space evolution module; the spatial analysis module is used for solving the problem of quantitative universal analysis of spatial information; the troubleshooting problem module is used for solving the problem of how to identify the urban space development problem; the space evolution module is used for solving the problem of guiding the space planning strategy from the space development problem.

The space analysis module of the urban space diagnosis model comprises a data comb plate management and analysis integrated plate, the problem troubleshooting module comprises a space diagnosis I plate, and the space evolution module comprises a space diagnosis II plate. The data combing plate is used for combing data related to spatial analysis so as to improve data cognition, clear paths and cost of data acquisition, realize data standardization and establish a feedback system required by a subsequent link. The analysis integration board aims to find the direction of space analysis by paying attention to new urban development hotspots and new urban policy requirements and absorbing new space analysis methods. The main purpose of the spatial diagnostic I-panel is to identify urban spatial development problems. Spatial diagnostic I panels resemble whole body physical examinations, while spatial diagnostic II panels resemble "specialist" diagnoses. The diagnosis I is mainly characterized by three, namely, the space problem in the urban area is found out through detecting, scanning and analyzing the overall data of the urban space; second, the assessment of the indicator of homogenization is not excluded; and thirdly, the automation can be basically realized.

Diagnosis II plate space diagnosis II needs to rely on experience advantages of planners to summarize a set of general analysis paths, and then customized analysis research is carried out by combining specific cases. Is a specialist diagnosis for specific problems. The supply and demand matching of quality, position and time is mainly concerned, and a spatial solution is provided. In addition, the working set of diagnosis II has the customized characteristic, the data and the technical route of the diagnosis II need to be adapted according to the local conditions, and only few urban problems can be automatically analyzed.

According to the scheme, the existing planning work methodology depending on individual subjective experience is converted into objectification, scientization and quantitative transformation through the original urban space diagnosis model, the underlying logic for constructing the urban space diagnosis system is quantitative analysis, and the urban space diagnosis system can be a powerful quantitative analysis tool for planning work to a certain extent and provides a more objective and convincing evidence for planning judgment; based on the urban space diagnosis technology, the management efficiency in the aspect of urban management is greatly improved; through the verification of multiple data sources, the city big data change is fed back in time, and the supervision efficiency of city planning is improved.

Drawings

The invention may be better understood by reference to the following description taken in conjunction with the accompanying drawings, in which like reference numerals identify like or similar parts throughout the figures. The accompanying drawings, which are incorporated in and form a part of this specification, illustrate preferred embodiments of the present invention and, together with the detailed description, serve to further explain the principles and advantages of the invention. On the attachment

In the figure:

FIG. 1 is a flow chart of a city space analysis diagnostic model;

FIG. 2 is a block diagram of a city space analysis and diagnosis system module;

FIG. 3 is a block diagram of a spatial analysis module;

FIG. 4 is a schematic diagram of an output result;

FIG. 5 is a graph of input-output relationships;

FIG. 6 is a schematic diagram of a display template for an automated general analysis;

FIG. 7 is a schematic diagram of the index pointing clarity principle of the spatial diagnostic I-module.

Detailed Description

Embodiments of the present invention will be described below with reference to the accompanying drawings. Elements and features depicted in one drawing or one embodiment of the invention may be combined with elements and features shown in one or more other drawings or embodiments. It should be noted that the figures and description omit representation and description of components and processes that are not relevant to the present invention, but known to those of ordinary skill in the art, for the sake of clarity.

The invention provides a full-period carbon assessment method and a full-period carbon assessment system for an urban update project, which adopt an original urban space diagnosis model to realize an urban space dynamic assessment and diagnosis system. The urban space diagnosis and research work is not only for producing a certain product, but also for creating an urban space basic research and development work chassis. The physical examination system is not only a simple city physical examination index set, but also a method set for providing space analysis for various planning projects, a tool box for solving the problem of city space and a compass for providing a city space development strategy.

Method for realizing informatization automation and systematization of urban space diagnosis

The informatization, automation and systematization characteristics of the urban spatial state assessment and diagnosis technology are the basis for the applicant to realize scientific and technological enabling and improve the efficiency of planning work in all aspects

The urban space diagnosis technology can improve the efficiency of data learning. With the development of various big data such as BIM, CIM and a large amount of open source data, the space-time precision of the data is also improved, huge city data can be utilized through the city space diagnosis system, and the quick iterative upgrade of the diagnosis system is realized by combining a machine learning method, so that the city development rule and new city changes can be better mastered.

The urban space diagnosis technology can improve the efficiency of planning work per se. In the fields of geographic research and urban research for a long time, quantitative research is always a hotspot, and a plurality of existing quantitative models can be integrated into daily planning work. Meanwhile, with the development of AI technology, the smart city field emphasizes the sharing and cooperation of cloud platforms, a corresponding city smart 'ecosystem' is created, and the city diagnosis system can be integrated with future smart city services of the applicant, so that the corresponding working efficiency is improved.

The urban space diagnosis technology can improve the management efficiency in the aspect of urban treatment. Through the verification of multiple data sources, the city big data change is fed back in time, and the monitoring efficiency of city planning is improved.

Secondly, the action plan is practiced, and the scientificity of the planning technical method is improved

From the idea of practicing the applicant's action planning, first the urban diagnosis system can improve the scientificity of our planning technique.

With the development of society and cities, elements needing to be considered in planning work are more and more numerous, and a planning work methodology which simply depends on personal subjective experience needs to be transformed to objectification, scientization and quantification. The bottom logic for constructing the urban space diagnosis system is quantitative analysis, and can become a powerful quantitative analysis tool for planning work to a certain extent, thereby providing a more objective and convincing evidence for planning judgment.

Secondly, the urban space diagnostic system can provide better urban space learning and monitoring tools. The coming of the urban operation era emphasizes that planning needs to be transformed from providing a good scheme to providing good service for urban development, one of the most basic requirements of urban service operation is that urban operation is well known and dynamic maintenance service is provided, and an urban space dynamic evaluation and diagnosis system can realize timely feedback and alert supervision of urban space information by means of multi-source data, so that the urban operation field can be better served.

In addition, the urban space diagnostic system can greatly improve the efficiency of data processing and application, and in the field of urban planning, a large amount of urban data is accumulated in each project department of the applicant for years, so that the urban space dynamic evaluation and diagnostic system can integrally improve the working efficiency of the project departments. Particularly, in a localized service project, services such as planning management implementation and the like are required to be continuously provided, which is particularly dependent on the automatic and informationized data processing capability of the space diagnosis system.

Finally, the urban space diagnosis system can introduce mature and advanced space metering methods, such as landscape ecology indexes and the like, into planning practice by constructing a high-quality diagnosis model. The city planning enters the 2.0 era, the original planning methodology can not completely solve the difficult and complicated symptoms faced by planners in city operation, and a multi-disciplinary theoretical method needs to be introduced. The scientificity and the comprehensiveness of planning work are improved, and the action planning idea is better practiced.

Providing customized service universal space diagnosis tool

As the applicant develops, the areas to be served and the types of projects to be accommodated are increasing. The applicant inherits the concept of providing customized service for each different region and project type, and needs an effective universal space diagnosis tool box in a large amount of urban data analysis work to realize the standardization and systematization of the planning production process.

For example, the situation of acquiring and using data in an actual project can be influenced by the presence or absence of data used in a city planning project and the use cost of the data, and at this time, some alternatives need to be proposed to solve the actual problem.

In addition, in the era of urban operation, the related work faced by planners is more complicated, and the new planners cannot necessarily grasp the experience-dominated urban planning work method quickly. And the urban space diagnosis system can assist planners to automatically analyze the whole-region analysis link, so that the learning cost of staff is reduced. Furthermore, the urban space diagnosis system realizes the integration of the whole flow of the planning work suite in a software mode, and new employees can use the tool to quickly start working and realize the reduction of the specification and the experience threshold of the production process.

Detailed description of the general embodiments

As a specific implementation, the general technical solution of the present invention is described in detail as follows: compared with the existing urban physical examination, the urban space diagnosis does not simply obtain an index set, and the index can be adjusted and diagnosed again after the space diagnosis is performed on the first place and the second place. But should be sublimated to the level of methodology. Therefore, the working logic of the urban space analysis and diagnosis system needs to be built from the foundation.

The framework of the urban space dynamic assessment and diagnosis system is shown in fig. 1, and the system is divided into three sequentially progressive flows according to the guidance for solving the space problem: spatial analysis, problem troubleshooting, and spatial evolution. With regard to the flows of the three layers, the space analysis needs to solve the problem of how to quantify the pervasive region of the urban space to analyze urban space information. The problem of troubleshooting is how to solve and identify the urban space development problem. The space development is to solve the problem of how to lead to the space planning strategy if the problem is identified in front of me.

The three flows of the spatial diagnostic system correspond to three problems to be solved, and the spatial analysis module is used for solving the problem of analyzing spatial information with quantitative universality; the main content of the troubleshooting problem module is to solve the problem of how to identify the urban space development; the space evolution module is mainly concerned with the problem of how to lead from the space development problem to the space planning strategy.

The three major processes of the space diagnosis system can be divided into four analysis blocks, see fig. 2, wherein the space analysis process comprises a data combing and analysis integrated block, the problem troubleshooting process corresponds to a space diagnosis I block, and the space evolution process corresponds to a space diagnosis II block.

And each plate has corresponding operation content and logic to ensure the smooth operation of each subsystem. The data combing plate is used for combing data related to spatial analysis so as to improve data cognition, clear paths and cost of data acquisition, realize data standardization and establish a feedback system required by a subsequent link. The analysis integration board aims to find the direction of space analysis by paying attention to new urban development hotspots and new urban policy requirements and absorbing new space analysis methods. The main purpose of the spatial diagnostic I-panel is to identify urban spatial development problems.

Spatial diagnostic I panels resemble whole body physical examinations, while spatial diagnostic II panels resemble "specialist" diagnoses. The diagnosis I is mainly characterized by three, namely, the space problem in the urban area is found out through detecting, scanning and analyzing the overall data of the urban space; second, index assessment without exclusion of homogenization; and thirdly, automation can be basically realized.

Diagnosis II plate space diagnosis II needs to rely on the experience advantages of planners to summarize a set of general analysis paths, and then customized analysis research is carried out by combining specific cases. Is a specialist diagnosis for specific problems. The supply and demand matching of quality, position and time is mainly concerned, and a spatial solution is provided. In addition, the working set of diagnosis II has the customized characteristic, the data and the technical route of the diagnosis II need to be adapted according to the local conditions, and only few urban problems can be automatically analyzed.

1. Spatial analysis module building framework

Referring to fig. 3, the analysis integration plate is classified according to two basic principles, modularization is realized, and the analysis integration system is divided into three modules from top to bottom layer by layer, namely, a total analysis module, a special analysis module and a progressive analysis module. The advantage of realizing modularization is that a complex space analysis system can be decomposed into several modules which are better controlled, and each module is a unit which can be combined, decomposed and replaced, and automation and software of space analysis can be realized.

One of the basic principles is first on the face and then inside, from simple to compound. Namely, the city space analysis of the integral surface property is firstly completed, and then the special space analysis of each subsystem of the city is deeply realized. The second principle is directed to a diagnosis link, that is, any type of spatial analysis module needs to be clearly directed to a certain type of urban space problem to support the starting and operation of the diagnosis module.

From a decomposition point of view, the overall analysis module mainly focuses on the overall scale, structure and spatial distribution description of the city operation core elements. And then selects the urban diagnosis index for diagnosing I. The special analysis module refers to the conditions of scale, spatial distribution, price and the like of the urban functional subsystem, and the subsystem can be subdivided by multiple iterations. The advanced analysis module has independent application value and is mainly used for judging whether the urban development accords with high-level value view or new concepts, such as job balance, carbon neutralization and the like. The special analysis and advanced analysis module can provide a composite index for problem identification of diagnosis I and can also provide a basis for problem tracking of diagnosis II.

2. Mapping relation of city data and basic analysis

Above we mentioned that in the data combing block we classify urban data into six major categories by spatial type, based on data of land, buildings, road networks, population, enterprises and facilities. Correspondingly, a corresponding mapping relation is constructed on the analysis integration plate. The overall analysis module is used for dividing the six types of city data into overall land analysis, overall building analysis, overall road network analysis, overall population analysis, overall industry analysis and overall facility analysis. Based on the overall analysis of six categories, the special analysis module can perform extended analysis, including green space and open space special analysis, public service facility special analysis, municipal facility special analysis, traffic facility special analysis, commercial facility special analysis, residence special analysis, storage factory special analysis, office building special analysis and the like. The advanced analysis module is combined with a high-level city idea to perform skyline analysis, city space structure analysis, life circle analysis, job and living balance analysis, city vitality analysis, TOD analysis, industry gathering area analysis, ecological pattern analysis and the like.

3. General requirements for output expression

Referring to fig. 4, the output result mainly includes "text" + two "drawings", that is, a piece of text and two maps. Because tables and charts are different visualizations of the same outcome, they are conventionally described as a "table/chart". A table may be shown as several charts in combination with classification, such as a result of "plot statistical description" which is "table/chart", which is essentially a "plot statistical table" + several "plot area distribution charts" shown respectively according to various plot types (see fig. 5 and 6).

4. Space diagnosis I module

Although the spatial diagnosis I module screens the problems of the whole city level, the spatial diagnosis I module needs to be distinguished from the urban physical examination on the market, on one hand, the spatial diagnosis I module should not only stay more and more than the screening indexes, but should adhere to the index pointing clear principle (figure 7), and specifically includes three requirements, namely problem focusing, limited index principle and flexible supplement, so that the diagnosis I module can further screen the urban problems from the index signs on the city surface, and can realize efficient and stable operation as much as possible.

To expand, the first is to adhere to problem focusing. Namely, the selection of indexes of the physical examination form focuses on the problems of urban construction level, such as land, industry, facilities and the like, and part of urban space benefit indexes, such as the space indexes of everyone and everywhere. The main objective is to let the physical examination index can sieve out the urban problem that space planning can solve. Second, a limited index. The problem investigation which is as complete and wide as possible needs to be realized by using a small amount of refined indexes (20-30), and simply speaking, the identification of a space problem in a certain aspect of a city is realized by using one group or one index. Third, flexible augmentation. On one hand, the urban space benefit indexes are increased under the condition of sufficient data support, and on the other hand, official urban physical examination indexes or certain special physical examination indexes can be flexibly supplemented according to specific project requirements.

On the other hand, the reasonable principle of the indexes is adhered to aiming at the design of single indexes. On the aspect of the characteristics of the indexes, the indexes which are easy to obtain, span-scale and have standards need to be selected; on the index type level, the indexes are subdivided into three types, namely single indexes, index combinations and comprehensive indexes, and each index can point to a certain type of urban space problem.

5. Spatial diagnostic II module

5.1 plate overview

The main purpose of the spatial diagnostics II panel is how to lead from the spatial development problem to the spatial planning strategy. The core content of the method is a diagnosis and treatment scheme of the space problem. The operation process form is that the diagnosis I 'physical examination report' and the data of diagnosis and treatment scheme design are input, and the urban space diagnosis planning suggestion is output. The main line of the design principle is to incorporate the common spatial problem analysis loop into the system as much as possible.

5.2 basic logic for diagnosis II

The urban management effect is taken as an entry point of the space diagnosis II, wherein the most core objects are supply and demand contradictions of various elements (land, facility and service) of the urban space, including supply and demand mismatch on scale (quantity), supply and demand mismatch on structure (quality, position and time) and actual effect of supplying the service. And taking the coupling degree of supply and demand matching and the effect of supply service as judgment criteria for measuring urban health.

5.3 basic procedure for diagnosis II

The basic idea of the space diagnosis II can be summarized into three stages, and whether the problem needs to be planned and solved or not is judged according to the urban problem identified by the diagnosis I module and the first certainty. And secondly, finding reasons, analyzing and tracking the reasons causing the problems. And a third party provides planning strategies and measures for the problems.

The qualitative and finding are the main processes which must be completed by the diagnosis II, but the problem is questioned by proposing planning strategies and measures for the problem, because the urban problem cannot directly correspond to the planning method, but is converted into the objective requirement on subsequent planning, and the 'evolution' may need to introduce more and more complex decision information.

5.4 Module architecture for diagnostic II

And constructing a module framework of the space diagnosis II according to the principle of combining bars and blocks and the working thought from the whole to the layout. The overall module focuses on three layers of diagnosis of central area problems, diagnosis of overall public space supply problems and diagnosis of urban green space systems. On the basis of the overall module, advanced diagnosis is carried out on the fields of industry, traffic, houses, public clothes, municipal administration and the like of urban areas. While the population-level diagnosis constitutes a separate analysis module.

By the scheme, the invention can realize the combination of the tracing and planning action schemes by pointing to the space problem, and has good expansibility.

Specifically, an embodiment of the present invention provides a method for evaluating a full-cycle carbon of an urban update project, including:

step 1: preprocessing the acquired data based on an AHP analytic hierarchy process, wherein the preprocessing specifically comprises the following steps: collecting city data to be diagnosed, wherein the city data comprises basic characteristic data and real-time characteristic data; wherein, the basic data is the existing data recorded in the city book, and the real-time data is the data changed in real time or the data of a preset target (which can have certain subjectivity);

step 2: performing basic analysis on the preprocessed acquired data: the acquired data comprises basic data and real-time data, the basic data and the real-time data comprise a plurality of data with attributes divided according to space types, and the basic analysis comprises function models corresponding to the data with different attributes in the acquired data;

and step 3: classifying and distinguishing the data Attribute characteristic values of the collected data according to the characteristic value distinguishing standard of the basic characteristic data and the characteristic value distinguishing standard of the real-time characteristic data by using the distinguishing method of each Attribute characteristic value of the basic characteristic data and the real-time characteristic data to form an Attribute characteristic value set AES (Attribute eigenvalue set) = { m' ₁ ,m’ ₂ ,…m’ _x ,n’ ₁ ,n’ ₂ ,…n’ _y And storing the data in a database, wherein x and y are natural numbers; wherein m' ₁ ,m’ ₂ ,…m’ _x Is base characteristic data, n' ₁ ,n’ ₂ ,…n’ _y Real-time characteristic data;

and 6: the method comprises the following steps of utilizing an urban space diagnosis model to analyze data, selecting one dimension or a plurality of dimensions to analyze, and specifically: during data analysis, a characteristic value set of a certain dimension and a corresponding evaluation index system are obtained and stored, and city evaluation data GGI = { g = 'in the dimension is obtained' ₁ ,g’ ₂ ,…g’ _z }。

In addition, the process can be displayed through a visual interface, and when the data analysis device is used, a technician only needs to input the relevant attribute values and the corresponding index threshold values according to the self needs, so that the data analysis result can be obtained. In addition, the visual interface can also be used for displaying real-time operation information of the simulation model or curve change of the mathematical model.

Specifically, the method for analyzing data by using the urban space diagnosis model comprises the following steps:

step A: the city data comprises a basic characteristic data set M and a real-time characteristic data set N, wherein M is marked as { M ₁ ,M ₂ ,…M _i ,…,M _x In which M _i Is given as { m } _i1 ,m _i2 ,m _i3 ,…,m _iti }, the list is expressed as follows:

wherein m is _ij Is the basic data attribute M _i Attribute feature value of (1), m _ij (i＝1,…,x；j＝1,…,t _i ) In the subscript (a), i means the i-th basic data attribute, and j means M _i J-th attribute feature value, t, of an attribute _i Is represented by M _i The number of attribute feature values of (2); eigenvalue discrimination method delta _i (i =1, \ 8230;, x) is the discrimination base data attribute group M _i (i =1, \8230;, x) a method of each characteristic value; the evaluation index set is a data set obtained by combining function models corresponding to data with different attributes in the collected data with the attribute characteristic value set; eigenvalue discrimination criterion sigma _ij Is to correspond to an underlying data attribute M _i Confirming each characteristic value m _ij (i＝1,…,x；j＝1,…,t _i ) A judgment standard value of (1);

and B, step B: real-time feature dataset N is noted as { N } ₁ ,N ₂ ,…N _i ,…,N _y In which N is _i Is given as { n } _i1 ,n _i2 ,n _i3 ,…,n _iri The list is expressed as follows:

wherein n is _ij Is running data attribute N _i N of the attribute feature value of _ij (i＝1,…,y；j＝1,…,r _i ) In the subscript (i) means the i-th run data attribute, and j means N _i J-th attribute feature value of attribute, r _i Is represented by N _i The number of attribute feature values of (2). Eigenvalue discrimination method Δ _x+i (i =1, \8230;, y) is the discriminating underlying data attribute group N _i (i =1, \8230;, y) a method of each characteristic value; the evaluation index set is a data set obtained by combining function models corresponding to data with different attributes in the collected data with the attribute characteristic value set; criterion η for discriminating characteristic value _ij Is corresponding to N _i Attribute validation each eigenvalue n _ij The discrimination standard value of (1);

and C: city evaluation data GGI, namely evaluation index system G, and is marked as G ₁ ,G ₂ ,…G _i ,…,G _z }; wherein, G _i Is marked as { g _i1 ,g _i2 ,g _i3 ,…,g _ipi },G _i The attribute feature value of (D) and the evaluation index set D of the basic feature data set M _i And an evaluation index set D of the real-time characteristic data set N _X+i Related, the list is expressed as follows:

evaluation index System G	Attribute eigenvalue
		G ₁	g ₁₁ ,g ₁₂ ,g ₁₃ ,…,g _1p1
G ₂	g ₂₁ ,g ₂₂ ,g ₂₃ ,…,g _2p2
		G _i	g _i1 ,g _i2 ,g _i3 ,…,g _ipi
…	…
		G _z	g _z1 ,g _z2 ,g _z3 ,…,g _zpz

In the present embodiment, the basic data is divided into data of 6 attributes by space type, and the 6 attributes are existing log data based on a land, existing log data based on a building, existing log data based on a road network, existing log data based on a population, existing log data based on an enterprise, and existing log data based on a facility, respectively. The real-time feature data is also classified into data of 6 attributes by spatial type, and the 6 attributes are estimated data based on a land, a building, a road network, a population, a business and a facility. The expected data may be data planned to be realized, data of a predetermined target, or data calculated to be changed in real time.

As a specific implementation scheme, the mapping relationship between city data and basic analysis is described by taking existing recorded data based on buildings as an example: the existing recorded data based on the building comprises seismic motion levels of the shared intensity, the basic intensity, the rare intensity and the super-fortification intensity of the existing building location, the estimated data based on the building comprises the seismic motion levels of the shared intensity, the basic intensity, the rare intensity and the super-fortification intensity of the prepared building location, the basic analysis comprises structural behavior parameters of the shared intensity, the basic intensity, the rare intensity and the super-fortification intensity, and the mapping relation is a seismic motion curve of the shared intensity, the basic intensity, the rare intensity and the super-fortification intensity established according to the structural behavior parameters.

In this embodiment, the urban space diagnosis model includes a plurality of function models, such as a mixed usage degree of land, a balance degree of employment, an ecological space diagnosis, a public complement diagnosis model, a building environment diagnosis, and the like. As a specific example, each function model is described as follows:

1. the function model of the land mixture use degree comprises the following steps:

A. land diversity:

in the formula: the H-land diversity coefficient is used for describing the abundance and the complexity of land types and reflecting the number of the land types and the proportion of various types; pk-proportion of land area of type K to total area; the type of N-plot.

B. Land utilization uniformity: e = H/Hmax

In the formula: e-land uniformity coefficient, H-land diversity coefficient, and Hmax-land diversity maximum value.

C. Land mixture degree:

in the formula: phh-land use mix ratio, RKMD-population density (man/hectare), EMK-class K employment post density (units/hectare).

2. The function model of the position balance degree comprises:

A. actual commuting distance:

in the formula: t is _act -actual commute distance, which is the average of the actual commutes of all commuters within the study; t is t _ij -representing the number of commutes of cells i to j in the commute OD matrix; c. C _ij -represents the commute distance from cell i to j; w-total commute number.

B. Theoretical maximum (minimum) commute distance:

the theoretical maximum (minimum) commute distance is the worst (optimal) commute situation, i.e. each occupant chooses the farthest (closest) post employment, while maintaining the urban spatial structure unchanged. In other words, the model treats the commuter distance matrix as a variable to seek the maximum and minimum values of the objective function.

Constraint conditions are as follows:

in the formula: t-average commute distance; x is the number of _ij -number of commuters for cells i to j at best; o is _j -number of occupants in cell i; d _j -number of posts of cell j; n-total number of residential cells; m-represents the total number of employment cells.

C. Excess commute rate:

in the formula: e _c -excess commute rate.

D. Balance of employment assessment: the surplus commute, the capacity utilization, the commute efficiency and the standard commute efficiency index can be utilized to evaluate the job and live balance and the commute efficiency.

In the formula: u-capacity utilization.

In the formula: c _e -commuting efficiency;

C _Ne -standard commute efficiency;

T _md -thereupon evaluating the commuting distance

3. Ecological space diagnosis:

A. the 8 ecological factors related to supporting biodiversity this ecological service function:

BD＝f(Pt,Ps,Ed,Ct,Co,Sp,Su,Hc-1)

in the formula: BD-ecological services supporting biodiversity; pt-plaque type; ps-plaque size; ed-boundary; ct-corridor type; co-gallery connectivity; hc-artificial corridor; sp-vegetation population; su-ecological succession process; hc-is inversely related to BD.

By performing relevant superposition operation of multiplication and division on the ecological factors in ArcGIS, the level of the green infrastructure supporting biodiversity of different spatial forms can be quantitatively evaluated.

B. Carbon regulation ecological service:

RC＝f(Sa,Tc,Tp,Ue-1)

in the formula, RC-carbon regulated ecological services; ue-urban heat island effect; sa-plot area; tc-average crown coverage; tp-proportion of healthy or big trees.

By performing multiplication and superposition operation on the ecological factors in ArcGIS, the providing degree of the ecological service to RC under different forms can be calculated.

4. Public complete diagnosis:

A. item public service facility reachability based on public transportation:

the time that the project can reach the public service facility:

in the formula: a. The _i -average reachable time of each entrance i of the project to all public service facilities; WS (WS) _ij -the shortest time for the project doorway i to walk to the public site; p _ij -the shortest time required to ride a public transport in the course of a project entrance i to a public service facility j; WH _ij -the bus stop walks to the public service facility j during the course of the project entrance/exit to the public service facility jThe shortest application time; n-the number of public service facilities in the scope of the study.

Frequency of public transportation service

In the formula: f, the frequency of public transportation service around the project; c _i -number of departure buses per hour of the route; m-total number of bus routes available for taking.

B. Accessibility of public transportation:

the reach distance matrix:

the arrival time matrix:

in the formula: s _ij 、T _ij Travel distance and travel time from i gate to j bus station (rail station gate).

In the formula: m is a group of _ij The matrix of the amount of ingress and egress OD from the i egress to the j bus stations (track station entrances and exits).

In the formula: s _i -shortest average distance from entrance/exit to bus stop (entrance/exit to track station) by bus (track) trip; t is a unit of _i Departure from the ith entrance to a bus stop (entrance to a rail station) and departure from the ith entranceShortest average time of row, instant null reachability.

5. Building environment diagnosis model:

A. wind environment:

and (3) simulating the spatial layout in green building evaluation software such as 'PKPM', and the like, and calculating the wind speed V1.5 and the wind speed amplification coefficient VSR at the position where the distance between pedestrian areas around the building is 1.5 meters higher under working conditions of winter, transition season and summer.

In the formula, VSR is the wind speed amplification coefficient; vmax-maximum wind speed at 1.5 meters above ground around the building; v-the same height wind speed on the open ground.

B. Thermal environment:

the average heat island strength was calculated by simulating the spatial layout in green building evaluation software such as "PKPM".

Wherein T-average thermal island strength (. Degree. C.); tp-mean temperature (. Degree. C.); tt-temperature increase of solar radiation (. Degree. C.); tc-long wave cooling by radiation (DEG C); tz-temperature reduction (DEG C) of evaporation heat exchange; td-typical meteorological temperature (. Degree. C.); n-8.

C. Light environment:

the visible light reflectance ratio of the glass curtain wall is as follows:

wherein ρ is a reflectance of visible light (380 nm to 780 nm); Φ trans-the luminous flux emitted by glass or other materials in the visible spectrum (380 nm-780 nm); Φ IN-the amount of light incident on glass or other materials in the visible spectrum (380 nm-780 nm).

The method of the present invention is not limited to being performed in the chronological order described in the specification, and may be performed in other chronological orders, in parallel, or independently. Therefore, the order of execution of the methods described in this specification does not limit the technical scope of the present invention.

While the present invention has been disclosed above by the description of specific embodiments thereof, it should be understood that all of the embodiments and examples described above are illustrative and not restrictive. Various modifications, improvements and equivalents of the invention may be devised by those skilled in the art within the spirit and scope of the appended claims. Such modifications, improvements and equivalents are also intended to be included within the scope of the present invention.

Claims

1. A full-period carbon assessment method for an urban update project is characterized by comprising the following steps: the method comprises the following steps:

step 1: preprocessing the collected data based on an AHP analytic hierarchy process, wherein the preprocessing specifically comprises the following steps: collecting city data to be diagnosed, wherein the city data comprises basic characteristic data and real-time characteristic data; the basic data is the existing data recorded in the city book, and the real-time data is the data changed in real time or the data of a preset target;

and step 3: classifying and distinguishing the data attribute characteristic values of the collected data by using the distinguishing method of each attribute characteristic value of the basic characteristic data and the real-time characteristic data according to the characteristic value distinguishing standard of the basic characteristic data and the characteristic value distinguishing standard of the real-time characteristic data to form an attribute characteristic value set AES = { m' ₁ ,m’ ₂ ,…m’ _x ,n’ ₁ ,n’ ₂ ,…n’ _y And storing the data in a database, wherein x and y are natural numbers; wherein, m' ₁ ,m’ ₂ ,…m’ _x Is a basic featureCharacterization data, n' ₁ ,n’ ₂ ,…n’ _y Real-time characteristic data;

and 4, step 4: forming an evaluation index system by using function models corresponding to data with different attributes in the collected data and combining the attribute feature value set AES;

and 5: combining the acquired data in the step 1, the basic analysis in the step 2, the attribute characteristic value set AES in the step 3 and the evaluation index system in the step 4 to construct an urban space diagnosis model, wherein the urban space diagnosis model comprises a mapping relation between the acquired data and the basic analysis;

2. The method for full cycle carbon assessment of a city update project of claim 1, wherein: the method for analyzing data by using the urban space diagnosis model specifically comprises the following steps:

wherein m is _ij Is the basic data attribute M _i Attribute feature value of (1), m _ij (i＝1,…,x；j＝1,…,t _i ) In the subscript (a), i means the i-th basic data attribute, and j means M _i J-th attribute feature value, t, of an attribute _i Is represented by M _i The number of attribute feature values of (2); characteristic value discrimination methodΔ _i (i =1, \8230;, x) is the discriminating underlying data attribute group M _i (i =1, \8230;, x) a method of each characteristic value; the evaluation index set is a data set obtained by combining function models corresponding to data with different attributes in the collected data with the attribute characteristic value set; eigenvalue discrimination standard sigma _ij Is to correspond to an underlying data attribute M _i Confirming each characteristic value m _ij (i＝1,…,x；j＝1,…,t _i ) A judgment standard value of (2);

and B: the real-time feature data set N is denoted as { N } ₁ ,N ₂ ,…N _i ,…,N _y In which N is _i Is given as { n } _i1 ,n _i2 ,n _i3 ,…,n _iri The list is expressed as follows:

wherein n is _ij Is running data attribute N _i Attribute feature value of n _ij (i＝1,…,y；j＝1,…,r _i ) In the subscript (i) means the i-th operation data attribute, and j means N _i J-th attribute feature value of attribute, r _i Is represented by N _i The number of attribute feature values of (2). Eigenvalue discrimination method Δ _x+i (i =1, \ 8230;, y) is the discrimination basis data attribute group N _i (i =1, \8230;, y) a method of each characteristic value; the evaluation index set is a data set obtained by combining function models corresponding to data with different attributes in the collected data with the attribute characteristic value set; criterion eta for distinguishing characteristic value _ij Is corresponding to N _i Attribute validation each eigenvalue n _ij The discrimination standard value of (1);

and C: city evaluation data GGI, namely evaluation index system G, and is marked as G ₁ ,G ₂ ,…G _i ,…,G _z }; wherein, G _i Is marked as { g _i1 ,g _i2 ,g _i3 ,…,g _ipi },G _i The attribute feature value of (D) and the evaluation index set D of the basic feature data set M _i And an evaluation index set D of the real-time characteristic data set N _X+i Correlation, tabulation tableShown below:

Wherein, g _ij Is G _i The attribute characteristic values listed by the city data characteristic evaluation attribute are specifically the evaluation index set D of the basic characteristic data set M _i And an evaluation index set D of the real-time characteristic data set N _X+i The corresponding sums are obtained, g _ij (i＝1,…,z；j＝1,…,p _i ) In the subscript (i) refers to the evaluation attribute of the characteristics of the ith city data, j refers toG _i J-th attribute feature value, p, of an attribute _i Is represented by G _i The number of attribute feature values of (2). i, j, p _i And z is a natural number.

3. The method for full-cycle carbon assessment of a city update project according to claim 1, wherein: the basic data is classified into 6 kinds of attributes by space type, and the 6 kinds of attributes are existing recorded data based on a land, a building, a road network, a population, a business and a facility, respectively.

4. The method of claim 3, wherein the method comprises: the real-time feature data is divided into data with 6 attributes according to space types, wherein the 6 attributes are respectively estimated data based on land use, estimated data based on buildings, estimated data based on road network, estimated data based on population, estimated data based on enterprises and estimated data based on facilities.

5. The method for full-cycle carbon assessment of a city update project according to claim 1, wherein: the urban space diagnosis model integrates a space quantitative analysis method which absorbs the front edge, and a dynamically-evaluated urban space diagnosis index system is built.

6. The method of claim 5 for full cycle carbon assessment of a city update project, wherein: the urban space diagnosis model is divided into three sequential progressive flows according to the direction for solving the space problem: spatial analysis, problem troubleshooting and spatial evolution; the spatial analysis aims to solve the problem of how to quantify a pervasive area in urban space to analyze urban space information; the troubleshooting problem is how to solve and identify the urban space development problem; the space evolution is just to solve the problem of how to lead to the space planning strategy when identifying the problem.